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FedDec: Peer-to-peer Aided Federated Learning

open access: yes2024 IEEE 25th International Workshop on Signal Processing Advances in Wireless Communications (SPAWC)
14 pages, 6 figures in png or pdf ...
Costantini, Marina   +2 more
openaire   +4 more sources

Faculty peer-to-peer learning and support online during difficult times: Main types of interactions and engagement during structured faculty conversations

open access: yesTo Improve the Academy
The Faculty Conversation Series at North Carolina State University, offered by the Office for Faculty Excellence, is a virtual option to connect faculty across campus to discuss topics immediately relevant to the unique teaching needs that
DIane D Chapman, Maria Gallardo-Williams
doaj   +2 more sources

Characteristics and comparison of peer-assisted learning interactions among university students in Harbin, China

open access: yesSocial Sciences and Humanities Open, 2021
Peer learning is gaining momentum as an educational model suitable for university students. This study investigates the characteristics of the participation in peer-assisted learning groups (PALG) for students in Harbin-China, and compares them to their ...
Oluwafolakemi Grace Ala   +2 more
doaj   +1 more source

A familiar peer improves students’ behavior patterns, attention, and performance when learning from video lectures

open access: yesInternational Journal of Educational Technology in Higher Education, 2023
Synchronous online learning via technology has become a major trend in institutions of higher education, allowing students to learn from video lectures alongside their peers online.
Zhongling Pi   +3 more
doaj   +1 more source

P2P4L2L (Peer to Peer for Learning to Learn)

open access: yes, 2020
Memòria del projecte P2P4L2L (Peer to Peer for Learning to Learn) 2016PID-UB / 024 llevado a cabo entre 2016 i 2018 sobre el disseny i desenvolupament de pràctiques d'avaluació entre iguals per desenvolupar la competència d'aprendre a aprendre.
Cano García, Elena   +7 more
openaire   +1 more source

MAPL: Model Agnostic Peer-to-peer Learning

open access: yesCoRR
Effective collaboration among heterogeneous clients in a decentralized setting is a rather unexplored avenue in the literature. To structurally address this, we introduce Model Agnostic Peer-to-peer Learning (coined as MAPL) a novel approach to simultaneously learn heterogeneous personalized models as well as a collaboration graph through peer-to-peer ...
Sayak Mukherjee   +2 more
openaire   +2 more sources

FedCoin: A Peer-to-Peer Payment System for Federated Learning [PDF]

open access: yes, 2020
Federated learning (FL) is an emerging collaborative machine learning method to train models on distributed datasets with privacy concerns. To properly incentivize data owners to contribute their efforts, Shapley Value (SV) is often adopted to fairly assess their contribution. However, the calculation of SV is time-consuming and computationally costly.
Yuan Liu 0002   +5 more
openaire   +3 more sources

Distributed Peer-to-Peer Optimization Based on Robust Reinforcement Learning with Demand Response: A Review

open access: yesComputers
The increasing adoption of renewable energy resources and the growing need for efficient and adaptable energy management have emphasized the importance of innovative solutions for energy sharing and storage.
Andrés Martínez, Paul Arévalo
doaj   +1 more source

Personalized and Private Peer-to-Peer Machine Learning

open access: yes, 2017
The rise of connected personal devices together with privacy concerns call for machine learning algorithms capable of leveraging the data of a large number of agents to learn personalized models under strong privacy requirements. In this paper, we introduce an efficient algorithm to address the above problem in a fully decentralized (peer-to-peer) and ...
Bellet, Aurélien   +3 more
openaire   +6 more sources

Peer-to-Peer Federated Learning: A Comprehensive Survey

open access: yesAI
The last five years have seen considerable growth in the topic of peer-to-peer (P2P) federated learning (FL). This framework removes the central coordinating server used in conventional federated learning and instead requires participating nodes to ...
Ashley Allen   +3 more
doaj   +1 more source

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